ordinal: Regression Models for Ordinal Data

Implementation of cumulative link (mixed) models also known
as ordered regression models, proportional odds models, proportional
hazards models for grouped survival times and ordered logit/probit/...
models. Estimation is via maximum likelihood and mixed models are fitted
with the Laplace approximation and adaptive Gauss-Hermite quadrature.
Multiple random effect terms are allowed and they may be nested, crossed or
partially nested/crossed. Restrictions of symmetry and equidistance can be
imposed on the thresholds (cut-points/intercepts). Standard model
methods are available (summary, anova, drop-methods, step,
confint, predict etc.) in addition to profile methods and slice
methods for visualizing the likelihood function and checking
convergence.